code: def _tf_is_indep_analytic(func, *args, **kwargs):
      """Test analytically whether a function is independent of its arguments
      using TensorFlow.
  
      Args:
          func (callable): Function to test for independence
          args (tuple): Arguments for the function with respect to which
              to test for independence
          kwargs (dict): Keyword arguments for the function at which
              (but not with respect to which) to test for independence
  
      Returns:
          bool: Whether the function seems to not depend on it ``args``
          analytically. That is, an output of ``True`` means that the
          ``args`` do *not* feed into the output.
  
      In TensorFlow, we test this by computing the Jacobian of the output(s)
      with respect to the arguments. If the Jacobian is ``None``, the output(s)
      is/are independent.
  
      .. note::
  
          Of all interfaces, this is currently the most robust for the
          ``is_independent`` functionality.
      """
      import tensorflow as tf  # pylint: disable=import-outside-toplevel
  
      with tf.GradientTape(persistent=True) as tape:
          out = func(*args, **kwargs)
  
      if isinstance(out, tuple):
          jac = [tape.jacobian(_out, args) for _out in out]
          return all(
              all(__jac is None for __jac in _jac) for _jac in jac
          )
  
      jac = tape.jacobian(out, args)
      return all(_jac is None for _jac in jac)
  
file_path: pennylane/pennylane/math/is_independent.py
imports: import warnings
  import numpy
  from autograd.tracer import isbox, new_box, trace_stack
  from autograd.core import VJPNode
  from pennylane import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: _tf_is_indep_analytic(func) and returns: all, all